Osteoporosis is a major public health threat for over 50% of the population over age 50. Despite its importance, osteoporosis is largely under-treated, with less than 20% of those recommended for testing being screened. With substantial reimbursement cuts being introduced by Medicare for bone densitometry by dual energy X-ray absorptiometry (DXA, the current clinical standard), with a sensitivity of DXA for fracture prediction of less than 50%, and with the rapidly increasing size of the aging population of the U.S., there is an urgent need for additional and more sensitive modalities than DXA for clinical assessment of fracture risk. Biomechanical Computed Tomography (BCT) has emerged as a powerful alternative to DXA. This CT-based technology creates a structural "finite element" model of a patient's bone from their CT scans, and subjects that model to virtual forces in order to provide an estimate of the strength of the bone. Well validated in cadaver studies and being a better predictor of bone strength than is bone mineral density by DXA, BCT has also been shown to be highly predictive of osteoporotic fractures in clinical research studies. However, robustness remains an issue - can the technique be used easily by non-experts in research and clinical environments? Addressing this issue, the overall goal of this research is to improve the robustness of our software, such that it can automatically analyze scans from a wide range of CT scanners and using a wide variety of CT acquisition protocols, including new low-dose protocols that limit radiation exposure to the patient. Such a robust BCT diagnostic tool could then be offered as a supplementary "add-on" analysis to many types of CT exams taken for other purposes such as CT colonography, pelvic, abdominal, and spine exams, thus reducing hospital costs, incurring no addition radiation to the patient, requiring no change in the CT acquisition protocols, and therefore greatly increasing the number of patients that could be screened at low cost. Specifically, we propose in this Phase-I project to combine expertise in computer vision, CT scanning, and biomechanics in order to develop an automated method of "phantomless" cross-calibration of CT scans for robust vertebral strength assessment. Focusing on the spine, our major tasks are to perform a series of clinical studies in which patients are scanned twice using a variety of CT acquisition protocols;develop a custom external-calibration phantom and use that to determine the effects of various CT acquisition parameters on scanning standardization;and use machine learning techniques to develop a "statistical atlas" of the spine for automation of all image processing. We will combine these efforts to develop a phantomless BCT method that accounts for differences in image quality due to variations in CT scanners and acquisition protocols, including low-dose protocols, and that does so in a highly automated fashion requiring minimal user expertise and input. Should this project be successful, future work will further refine the techniques, extend them to the hip and quantitative analysis of muscle and other soft tissues, and address robustness of longitudinal changes for clinical monitoring. PUBLIC HEALTH RELEVANCE: With a mortality rate up to 30% one year after hip fracture, and an economic burden exceeding $17 billion annually, osteoporotic fracture is a debilitating condition whose impact on our aging society is growing. Early identification of those at risk for fracture can guide prevention and treatment, and BCT will provide a means for such detection with a sensitivity and specificity lacking in DXA based bone densitometry. The greater radiation exposure from CT, however, limits the market for such a diagnostic. The proposed project will result in a robust diagnostic test that significantly lowers radiation dose to the patient, and in some implementations, completely eliminates additional radiation by using CT scans already ordered for other medical purposes. Successful development of this product will broaden the pool of individuals who will benefit from a more accurate and sensitive fracture risk prediction, expand the market for O. N. Diagnostics'business, and result in an important advance in the preventative care and treatment of osteoporosis.